I would like to fine the pre-trained RetinaNet model available in torchvision in order to create my own object detection. I'm trying to replicate what is done for the FastRCNN at this link: https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html#finetuning-from-a-pretrained-model What I have done is the following: model = model = torchvision.models.detection.retinanet_resnet50_fpn(pretrained=True) num_classes = 2 # get number of input features and anchor boxed for the classifier in_features = model.head.classification_head.conv[0].in_channels num_anchors = model.head.classification_head.num_anchors # replace the pre-trained head with a new one model.head = RetinaNetHead(in_features, num_anchors, …

Let us say I have the loading images from my local files using the pytorch torchvision datasets.ImageFolder as follows: train_data = datasets.ImageFolder( os.path.join(out_dir, "Training"), transform=transforms.Compose([ transforms.Resize([224, 224]), # alenet image size transforms.ToTensor() # so that we will be able to calculate mean and std ]) ) How can I efficiently calculate the means and stds for each color channel I know when loading dataset from torchvision.dataset I can do it as follows: train_data = datasets.CIFAR10('.', train=True, download=True ) means = …

I am using read_image to read the image. from torchvision.io import read_image image = read_image("/content/train/000001-11.jpg") Now, when I try to find the shape of the image, I get $(4, 460, 513)$ as the image shape. But, when I use opencv to read the image, I get $(460, 513, 3)$ as the image shape. img=cv2.imread("/content/train/000001-11.jpg") Could anyone explain to me why this happens? Why are there 4 channels instead of three? I tried to print the 4 channels for a particular …

On the PyTorch documentation for torchvision.models, it is states that images have to be loaded in a range of [0,1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. What is the logic behind these specific values?